Aim:
  1. How do we find the line of best fit for a set of data?
  2. How can we use the least-squares line to predict unknown values? (You will not be asked this on the Regents)

At the end of the lesson, you will be able to...
  1. define and draw a scatter plot from a set of data
  2. define regression, the least squares line, and regression coefficients
  3. state the properties of the least squares line
  4. define the correlation coefficient
  5. use the graphing calculator to find the equation of the line of best fit
  6. use the equation found to predict values

This first video shows how to use the graphing calculator and on way to check your answer. You can also check your answer by entering your answer into the y= function.

Copy down the steps how to enter the data and get the answer from your graphing calculator.

First video


This next video is a very good video from James. It shows how we can use the equation you get from the graphing calculator and apply it to problems. You will need to know how to do this for the Regents.
*Please note the importance of r: the closer r is to +1 or -1, the better the choice. You will use this more in the next lesson.



This next video shows how to use the least squares method. This is just an FYI.



Classwork: Solve the following problem. Use your graphing calculator

CW_Linear_Regress.JPG

Homework: Do all problems. Use your graphing calculator.

Lin_Reg.JPG

Lin_Reg_2.JPG

Answers:

A-LinReg.JPG

A-LinReg2.JPG

Journal entry: When the sales volume (in hundreds of units) is plotted along the x-axis, and the money spent on advertising (in thousands of dollars) is plotted along the y-axis, researchers obtained an equation y=14x + 0.7. If $10,000 was spent do you have enough information to figure out the exact sales volume? Explain.